Instructions to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
# Run inference directly in the terminal:
llama cli -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
# Run inference directly in the terminal:
llama cli -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
# Run inference directly in the terminal:
./llama-cli -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Use Docker

docker model run hf.co/w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

LM Studio
Jan

vLLM

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "w-ahmad/Qwen3.5-9B-GGUF-MoQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "w-ahmad/Qwen3.5-9B-GGUF-MoQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Ollama
How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with Ollama:
```
ollama run hf.co/w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
```
Unsloth Desktop

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with Docker Model Runner:
```
docker model run hf.co/w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
```

Lemonade

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Run and chat with the model

lemonade run user.Qwen3.5-9B-GGUF-MoQ-BF16

List all available models

lemonade list

Hermes Agent

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

🚀 MoQ: Mixture of Quants

MoQ (Mixture of Quants) is a smart way to shrink AI models without losing their "brainpower." Unlike old methods that treat every part of the model the same, MoQ identifies the most important parts and keeps them high-quality, while heavily compressing the rest to save space.**Stop settling for uniform bitrates. Standard quantization is a relic of the past, treating vital cognitive weights the same as redundant noise. **

The result? A model that punches significantly above its weight class.

Benjamin Marie evaluated MoQ GGUFs ("Mixture of Quants") against Unsloth Dynamic (UD) quants, focusing on low-bit versions below 4 bits on average — the range where GGUF models typically struggle most. Results: At similar bits-per-weight (Bpw), MoQ outperforms Unsloth Dynamic quants by ~10% on benchmarks, while also being roughly 2× more token-efficient on average.

"MoQ models are much better than UD quants on benchmarks, and they are also more token-efficient."

Table

Folder Link	BPW	Total Size
📂 BF16	16	17.92 GB
📂 F16	16	17.92 GB
📂 MoQ-Quants-Latest	3.2	3.58 GB
📂 MoQ-Quants-Latest	3.6	4.03 GB
📂 MoQ-Quants-Latest	3.8	4.22 GB
📂 MoQ-Quants-Latest	4.1	4.64 GB
📂 MoQ-Quants-Latest	4.3	4.84 GB
📂 MoQ-Quants-Latest	4.6	5.11 GB
📂 MoQ-Quants-Latest	4.8	5.37 GB
📂 MoQ-Quants-Latest	4.9	5.49 GB
📂 MoQ-Quants-Latest	5.1	5.75 GB
📂 MoQ-Quants-Latest	5.3	5.92 GB
📂 MoQ-Quants-Latest	6.6	7.36 GB
📂 MoQ-mmproj	16.0	0.92 GB

🧠 The MoQ Edge

MoQ optimizes the architecture for the Pareto frontier of memory and performance.

Dynamic Bitrate Allocation: No more "one-size-fits-all." MoQ assigns precision where it actually matters.
Cognitive Preservation: Massive VRAM savings with near-zero degradation in logic and coherence.
Next-Gen Efficiency: Fits "Large" model intelligence into "Small" model hardware.

Comparison

Here is the comparison between MoQ and Unsloth dynamic quants. MoQ perform better i guess . Performed on wiki text (benchmaxxing is not allowed!!!)

x : https://x.com/WaleedAhmad1a10 If MoQ does not perform well, email me : waleedahmad.1a10@gmail.com

🛠 Usage & Deployment.

./llama-cli -m Qwen3.5-9B-MoQ-4.85.gguf -p "The future of efficient AI is..."

Downloads last month: 2,894

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

16-bit

View +12 variants

Model tree for w-ahmad/Qwen3.5-9B-GGUF-MoQ

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

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Collection including w-ahmad/Qwen3.5-9B-GGUF-MoQ

MoQ GGUFs

Collection

Mixture of Quants . • 9 items • Updated Jun 27 • 5